PROJECT SUMMARY/ABSTRACT Antimicrobial resistant (AR) pathogens remain a major cause of healthcare associated infections (HAIs) in the United States. Indeed, the prevalence of these existing and emerging drug-resistant agents continues to impose a heavy burden on U.S. healthcare systems. To better control existing AR pathogen-associated HAIs and prepare for the possible emergence of a novel AR organism, better, more targeted identification and intervention strategies need to be developed. Here, for this Modeling Infectious Diseases in Healthcare Research Projects to Improve Prevention Research and Healthcare Delivery (MInD Healthcare) network project, we propose to develop a hierarchy of new model-inference systems capable of simulating and forecasting HAI outbreaks, quantifying individual patient colonization risk, and identifying optimal intervention approaches. Specifically, we will use hospitalization records and diagnostic data for multiple AR pathogens from four major hospitals in New York City to conduct a series of modeling studies. We will develop two mathematical modeling structures: 1) a metapopulation model capable of simulating AR pathogen transmission dynamics across multiple healthcare facilities; and 2) an agent-based model capable of simulating individual- level patient infection status, transmission dynamics, and movements within multiple hospitals. These models will be used in conjunction with Bayesian inference methods to simulate observed outbreaks of AR pathogens, estimate critical epidemiological characteristics and asymptomatic carriage probabilities among individual patients, and support development of an AR pathogen forecasting system. As the models are high dimension and the observations are sparse, new inference methods, capable of data augmentation and efficient model optimization, will also be developed. Additionally, we will use the optimized model structures to run free simulations testing the effectiveness of six interventions: 1) hand hygiene and barrier precautions; 2) isolation of infections; 3) environmental cleaning; 4) active patient screening within hospitals; 5) contact tracing; and 6) screening at admission. These interventions will be tested singly and in bundles and used to inform targeted control approaches. Further, we will develop a framework for identifying intervention bundles that maximally reduce HAI rates given cost and logistical constraints. Lastly, we propose to collaborate with the CDC and the other research groups in the MInD Healthcare network to develop standardized intervention scenarios and inter-comparisons of simulated intervention outcomes among the different model forms used across the network.